In this post, we will be using a CNN for a text classification task. It will be very similar to the previous text classification task we did using RNNs but this time we will use a CNN in order to process the sequences. Text Classification The dataset for this task is the sentence polarity dataset v1.0 … Continue reading Convolutional Text Classification

In the previous post, we discussed the basics of policy gradients for reinforcement learning tasks. In our multi-armed bandit implementation, the reward was immediate in terms of wether the action we took was good or bad. But it many RL tasks, the reward may be delayed where we won't know the precise impact of our action … Continue reading Reinforcement Learning (RL) – Policy Gradients II

This is the first of a series of posts covering the basics of reinforcement learning (RL). There are several subsections under RL such as policy gradients, Q-learning, etc. In this post, we will specifically cover policy gradients, as it has many advantages over value and model based learning. But the reason I prefer it is … Continue reading Reinforcement Learning (RL) – Policy Gradients I

oIn this post, we will be covering the encoder-decoder architecture with attention for seq-seq tasks. We will loosely follow the implementation from the paper I have simplified here. First we will take a look at the entire model and talk about some interest parts, then we will break down attention and then we will start … Continue reading Recurrent Neural Network (RNN) – Part 4: Attentional Interfaces

In this post, I will cover the basic encoder-decoder which we use to process seq-seq tasks such as machine translation. We will not be covering attention in this post but we will implement it in the next one. Here we will feed in the input sequence into the encoder which will generate a final hidden … Continue reading RECURRENT NEURAL NETWORKS (RNN) – PART 3: Encoder-Decoder

In this post, we will explore the idea of creating our own custom RNN cells. But first, we will take a closer look at the simple RNN and then more complicated units such as LSTM and GRU. We will also analyze the tensorflow code for these units and draw from them to eventually create our … Continue reading Recurrent Neural Network (RNN) – Part 5: Custom Cells